Speech enhancement algorithms are often trained using a mean squared error (MSE) loss function. However, it has been shown that in most cases this approach is suboptimal [1]}. This is due to the fact that the quality metrics of interest are perceptual scores such as Perceptual Evaluation of Speech Quality (PESQ) [2]} and Short-Time Objective Intelligibility Measure (STOI) [3]}, which do not correlate perfectly with MSE. These metrics are sensitive to perceptually important effects such as differences in loudness, threshold effects and correlations, which the MSE loss fails to capture [4]}.
